Human-Machine Cooperative Research Articles

Anti-Rollover Trajectory Planning Method for Heavy Vehicles in Human–Machine Cooperative Driving

The existing trajectory planning research mainly considers the safety of the obstacle avoidance process rather than the anti-rollover requirements of heavy vehicles. When there are driving risks such as rollover and collision, how to coordinate the game relationship between the two is the key technical problem to realizing the anti-rollover trajectory planning under the condition of driving risk triggering. Given the above problems, this paper studies the non-cooperative game model construction method of the obstacle avoidance process that integrates the vehicle driving risk in a complex traffic environment. Then it obtains the obstacle avoidance area that satisfies both the collision and rollover profit requirements based on the Nash equilibrium. A Kmeans-SMOTE risk clustering fusion is proposed in this paper, in which more sampling points are supplemented by the SMOTE oversampling method, and then the ideal obstacle avoidance area is obtained through clustering algorithm fusion to determine the optimal feasible area for obstacle avoidance trajectory planning. On this basis, to solve the convergence problems of the existing multi-objective particle swarm optimization algorithm and analyze the influence of weight parameters and the diversity of the optimization process, this paper proposes an anti-rollover trajectory planning method based on the improved cosine variable weight factor MOPSO algorithm. The simulation results show that the trajectory obtained based on the method proposed in this paper can effectively improve the anti-rollover performance of the controlled vehicle while avoiding obstacles.

Cooperative Control Research on Emergency Collision Avoidance of Human–Machine Cooperative Driving Vehicles

Cooperative Control Research on Emergency Collision Avoidance of Human–Machine Cooperative Driving Vehicles

Human–Machine Cooperative Steering Control Based on Non-cooperative Nash Game

Human–Machine Cooperative Steering Control Based on Non-cooperative Nash Game

Game Theory-Based Interactive Control for Human–Machine Cooperative Driving

To address the safety and efficient driving issues of human–machine shared control vehicles (HSCVs) in future complex traffic environments, this paper proposes a game theory-based interactive control method between HSCVs and surrounding autonomous vehicles (SVs) and involves considering different driving behaviors. In HSCV, a comprehensive driver model integrating steering control and speed control is designed based on the brain emotional learning circuit model (BELCM), and the control authority between the driver and the automation system is dynamically allocated through the evaluation of the driving safety field. Factors such as driving safety and travel efficiency that reflect personalized driving style are considered for modeling the uncertain behavior of SVs. In the interaction between HSCVs and SVs, a method based on game theory and distributed model predictive control (DMPC) that considers the uncertainty of SVs’ driving behavior is established and is finally integrated into a multi-objective constraint problem. The driver control input in an HSCV will also be introduced into the solution process. To demonstrate the feasibility of the proposed method, two test scenarios considering the driving characteristics of different SVs are established. The test results show that automation control systems can promptly stop the human driver’s dangerous operations in an HSCV and safely interact with multiple AVs with different driving characteristics.

Human–Machine Cooperative Driving and Real-Time Algorithm Implementation Based on Double-Loop Control System

The double loop structure has been implemented to verify the feasibility of human-machine cooperative control in real environment in this paper, which can fully reflect the performance of the controller and realization of the sharing steering control between human driver and automatic control system. The outer loop adopts fuzzy proportional-integral-derivative(PID) controller to deal with different working conditions, on which the complexity of the algorithm is reduced and the real-time performance of the controller is improved. The inner loop adopts the active disturbance rejection controller (ADRC), which can effectively suppress the interference such as drivers’ faulty operations etc. and improve the tracking accuracy and system performance. The stability for extended state observe(ESO) and ADRC is described in theory to ensure the feasibility of the controller in the real world environment. The experiment results show that the human-machine cooperative control system based on the double-loop controller can effectively reduce the lateral error of the vehicle and the steering torque of the driver in the process. And the control performance of the vehicle is improved.

An Interactive Training Model for Myoelectric Regression Control Based on Human–Machine Cooperative Performance

Electromyography-based wearable biosensors are used for prosthetic control. Machine learning prosthetic controllers are based on classification and regression models. The advantage of the regression approach is that it permits us to obtain a smoother and more natural controller. However, the existing training methods for regression-based solutions is the same as the training protocol used in the classification approach, where only a finite set of movements are trained. In this paper, we present a novel training protocol for myoelectric regression-based solutions that include a feedback term that allows us to explore more than a finite set of movements and is automatically adjusted according to real-time performance of the subject during the training session. Consequently, the algorithm distributes the training time efficiently, focusing on the movements where the performance is worse and optimizing the training for each user. We tested and compared the existing and new training strategies in 20 able-bodied participants and 4 amputees. The results show that the novel training procedure autonomously produces a better training session. As a result, the new controller outperforms the one trained with the existing method: for the able-bodied participants, the average number of targets hit is increased from 86% to 95% and the path efficiency from 40% to 84%, while for the subjects with limb deficiencies, the completion rate is increased from 58% to 69% and the path efficiency from 24% to 56%.

Data-Learning Game Output Regulation Approach for Human–Machine Cooperative Driving Toward Varied Drivers and Vehicles

Data-Learning Game Output Regulation Approach for Human–Machine Cooperative Driving Toward Varied Drivers and Vehicles

Organizational digital transformation: from evolution to future trends

PurposeThis article explores the emergence of organizational digital transformation in the rapidly advancing technological era. It discusses the origins, driving forces, strategies, challenges and broader implications.Design/methodology/approachThe article employs a scoping review methodology that synthesizes knowledge from the existing literature, research, case studies and other relevant sources.FindingsThe findings underscore the pivotal role that organizational digital transformation plays in an era of relentless technological advancement. Leadership, organizational culture and technological enablers are identified as crucial drivers of innovation and competitiveness within organizations. The article also emphasizes ethics as a crucial element of digital transformation, focusing in particular on concerns about data privacy and the morality of artificial intelligence. Additionally, the author talks about anticipated future trends that are anticipated to influence the future of digital transformation, such as the growing influence of artificial intelligence, the trend toward hyper-personalization and the emergence of quantum computing.Research limitations/implicationsThe assessment has failed to provide recommendations for the actual implementation because it has mainly concentrated on conceptual and strategic aspects. Furthermore, it does not clearly define the criteria for choosing real-world examples, which limits the representation of the different industries, size ranges of organizations and outcomes associated with digital transformation.Practical implicationsThe article stresses the significance of paying attention to the forces driving digital transformation while navigating ethical and societal concerns. In addition to highlighting the importance of anticipating future trends for strategic planning in the rapidly changing digital landscape, it emphasizes the advantages as incentives for organizations to invest in digital initiatives.Social implicationsThe investigation demonstrates how technology contributes to progress while posing complex ethical and change management issues. In light of increased connectivity, data analytics and artificial intelligence, it highlights the crucial need for societal adaptability and highlights the crucial role that cooperative human–machine coexistence plays in responsible development and transformative societal evolution.Originality/valueThe article stands out because it examines organizational digital transformation in-depth while considering its historical roots, ethical implications and future prospects. It is a priceless contribution to the field because real-world case studies and a scoping review provide a distinctive viewpoint and a comprehensive view of the effects of digital transformation on organizations and society.

HMPT: a human–machine cooperative program translation method

Program translation aims to translate one kind of programming language to another, e.g., from Python to Java. Due to the inefficiency of translation rules construction with pure human effort (software engineer) and the low quality of machine translation results with pure machine effort, it is suggested to implement program translation in a human–machine cooperative way. However, existing human–machine program translation methods fail to utilize the human’s ability effectively, which require human to post-edit the results (i.e., statically modified directly on the model generated code). To solve this problem, we propose HMPT (Human-Machine Program Translation), a novel method that achieves program translation based on human–machine cooperation. It can (1) reduce the human effort by introducing a prefix-based interactive protocol that feeds the human’s edit into the model as the prefix and regenerates better output code, and (2) reduce the interactive response time resulted by excessive program length in the regeneration process from two aspects: avoiding duplicate prefix generation with cache attention information, as well as reducing invalid suffix generation by splicing the suffix of the results. The experiments are conducted on two real datasets. Results show compared to the baselines, our method reduces the human effort up to 73.5% at the token level and reduces the response time up to 76.1%.

Human–Machine Cooperative Decision Making Outperforms Individualism and Autonomy

The experiment reported in this article provides a first experimental evaluation of human–machine cooperation on decision level: It explicitly focuses on the interaction of human and machine in cooperative decision-making situations for which a suitable experimental design is introduced. Furthermore, it challenges conventional leader–follower approaches by comparing them to newly proposed automation designs based on cooperative decision-making models. These models originate from negotiation theory and game theory and allow for an investigation of cooperative decision making between equal partners. This equality is motivated by similar approaches on the action level of human–machine cooperation. The experiment's results indicate an added value of the proposed automation designs in terms of objective cooperative performance as well as human trust in and satisfaction with the cooperation. Hence, the experiment yields the same insight on decision level as already observed on action level: It may be beneficial to design machines as equal cooperation partners and in accordance to models of emancipated human–machine cooperation.

Security-Based Resilient Triggered Output Feedback Lane Keeping Control for Human–Machine Cooperative Steering Intelligent Heavy Truck Under Denial-of-Service Attacks

This paper addresses dynamic authority allocation strategy and security-based resilient event triggered output feedback lane keeping control (LKC) problem for human-machine cooperative (HMC) steering intelligent heavy truck subject to non-periodic energy bounded denial-of-service (DoS) attacks. For authority allocation strategy of considered LKC system, designed practical and easily-to-implement cooperative allocation factor is adaptively adjusted by the correlation characteristics between buffered interested states for the first time. For derived fuzzy model of networked LKC system with time-varying cooperative allocation factor and path tracking velocity, a new security-based resilient event triggered output feedback control with communication scheme consisting of adaptive adjusted auxiliary variable and attack-induced random but bounded uncertainty is proposed to reduce communication burden when free of attacks and simultaneously release necessary data for stability maintenance at the cost of predefined allowable control performance degradation when attack-induced uncertainty less than its upper bound. Furthermore, sufficient conditions are derived for exponentially stability of considered LKC system with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H\infty $</tex-math></inline-formula> performance, gain matrices for controller and trigger are co-designed and obtained by solving certain matrix inequalities. Benefiting from saved communication resource and more sparse but necessary assistant torque commands under resilient event triggered communication scheme, as well as dynamic authority allocation strategy with adaptive cooperative allocation factor, the obtained results illustrating fewer driving conflict, less driver effort and higher resistant ability to attack-induced yaw instability demonstrate the effectiveness of the proposed control strategy.

Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation

Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human-machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference.

Indirect Shared Control Strategy for Human-Machine Cooperative Driving on Hazardous Curvy Roads

Effective and friendly human-machine cooperative driving is considered as a feasible solution to filling the gap between assisted driving and highly automated driving. This paper proposes an indirect shared control strategy, in both longitudinal and lateral directions, for safe cooperative driving on curvy roads, which often pose hazards in real-world driving. At the tactical decision level of the proposed strategy, driving authorities are dynamically allocated between human and machine based on the risk assessed with a data-driven gaussian processes regression (GPR) model, which is trained to generate dynamic safety envelopes of longitudinal and lateral control commands, namely vehicle speed and front wheel steering angle, from observed road conditions and vehicle states. At the maneuver execution level, a multi-objective hierarchical MPC controller is built to combine human-machine control commands smoothly for collision-free driving while simultaneously maintaining vehicle stability and mitigating human-machine conflicts. Both matching the driver's commands and tracking the reference trajectories are formulated into objectives of the optimization-based controller. The weight factor of each objective is adjusted according to the authorities allocated at the decision level. The results of experiments conducted on a 3 degree of freedom motion-base driving simulator show that the proposed strategy accomplishes effective and friendly cooperative driving on curvy roads. It's also verified that the proposed strategy is adaptable to unseen scenarios, mainly owing to the superiority of the GPR-based risk assessment model, driven by class-balanced training data, in modeling the nonlinearity and uncertainty of driving risks.

Research on Control Authority Transfer Algorithm for the Intelligent Vehicle with Human-machine Cooperative Driving

Research on Control Authority Transfer Algorithm for the Intelligent Vehicle with Human-machine Cooperative Driving

Coordination Control Strategy for Human-Machine Cooperative Steering of Intelligent Vehicles: A Reinforcement Learning Approach

This paper proposes a novel coordination control strategy through reinforcement learning approach for human-machine cooperative steering of intelligent vehicles, so as to realize a more flexible and efficient way for the human driver and the automated driving system to jointly complete the path-following. Firstly, the human-machine cooperative steering is modeled by the optimal preview driver model with adaptive preview time and the MPC lateral tracking model with adaptive prediction horizon and step, as well as the steering system with a realistic dual-motor steering-by-wire structure is also built into the human-machine cooperative steering system. Then, the human-machine coordination control strategy is designed and trained based on two reinforcement learning agents to further optimize the allocation of cooperative steering weights. Finally, the strategy is fully verified in lane departure simulation scenarios and also in real-world Steering-in-the-Loop experiments with real-time controller, which shows that both agents can effectively keep the vehicle with the small lateral and yaw errors in the simulation scenarios, and also can be successfully applied in the realistic human-machine cooperative system to maintain the lateral and yaw errors within a safe range during path-following.

Human–Machine Cooperative Steering Control Considering Mitigating Human–Machine Conflict Based on Driver Trust

To reduce the impact of human–machine conflict on vehicle safety, this study proposes a novel human–machine cooperative steering control approach from the perspective of driver trust in the machine. The relationship between driver trust in the machine and driving skill is analyzed by the chi-square test method, and an online cooperative algorithm is designed using fuzzy control for different conditions, which assigns control authority based on driver trust under safe conditions and gives most of the authority to the machine to ensure safety under dangerous conditions. The machine is designed using model predictive control as an alternative controller parallel to the driver. To implement the proposed approach, a simulation platform that includes drivers and a test vehicle is established. Based on the driving data of human drivers collected in field tests, a two-point visual driver model is established to simulate steering behaviors and reflect physical workload. The parameters of the driver model are identified by a particle swarm optimization method to represent different drivers. The effectiveness of the approach, such as guaranteeing vehicle safety and reducing physical workload and human–machine conflict, is verified by simulations under typical conditions and obstacle avoidance conditions based on veDYNA vehicle dynamics software.

Human-Machine Cooperative Trajectory Planning and Tracking for Safe Automated Driving

This paper investigates a human-machine cooperative trajectory planning and tracking control approach for automated vehicles. The proposed method is developed based on a novel algorithm of cooperative human-machine rapidly-exploring random (HM-RRT) for path planning, together with the risk assessment of driver behavior. First, the driver’s behaviour is assessed according to the information of the predicted vehicle trajectory, the identified safe driving area and the driving risks evaluated in both lateral and longitudinal directions. Based on the driver’s expected driving task, when driving risks are identified by real-time assessment, then the human-machine cooperation is activated during trajectory planning. By HM-RRT, the newly developed safety assurance mechanism for path planning, the cooperative trajectory is then generated, which incorporates the driver’s desire and actions and automation’s corrective actions, to ensure the safety, stability and smoothness of the human-vehicle system. The simulation and experimental results show that the proposed HM-RRT algorithm can effectively improve the convergence rate and reduce the computation load, comparing to the conventional method. Beyond this, the proposed human-machine cooperation approach is able to simultaneously ensure the safety, stability and smoothness of the vehicle and largely reduce human-machine conflicts in real-time applications, demonstrating its feasibility and effectiveness.

Design and evaluation of cooperative human–machine interface for changing lanes in conditional driving automation

This study analyzes the impact of cooperative human–machine interface designs on drivers’ trust in and interaction with automated driving systems (ADSs) during lane changes on highways. While drivers’ inappropriate trust in ADSs can affect their behavior toward the system, capability to detect inadequate system performance, and perception of surrounding traffic disturbances, their engagement in the automated process can improve their comprehension of the system and traffic conditions, which is necessary for the safe practice of automated driving. Forty drivers practiced conditional driving automation in a driving simulator and encountered traffic congestion on the main lane of a two-lane highway. Four ADS designs were proposed to bypass the congestion. ADS-1 detects the congestion and synchronizes the speed accordingly. ADS-2 requests the driver to resume manual control and overtake the congestion. ADS-3 requests the driver to push a button to let the system overtakes the congestion automatically. ADS-4 overtakes the congestion automatically after informing the driver, while the driver can cancel it by pushing a button within 6 s. In all these conditions, driver intervention was optional. Although the drivers preferred and trusted ADS-1 and ADS-2 more than ADS-3 and ADS-4, the results indicate significant improvements in the driving performance and system usage under ADS-3 and ADS-4. Driving with ADS-3 improved drivers’ engagement and reduced the requirement for control transfer compared with other systems. However, the time headway between the subject and adjacent vehicles indicated that lane changes were more critical under ADS-3 and ADS-4 than ADS-1 and ADS-2. Such deficiency of alignment between driver perception and safe behaviors has implications for the design of future studies and systems that need to balance satisfaction and safety. These observations are likely to improve driver interaction with automated vehicles.

Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts.

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., “no fever,” “no cough,” and “no hypertension”) in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

New machine behavior’s evolutionary approaches between AI learning, control and ethics promoting cooperative Human-Machine Intelligence

The technological revolution in the fields of robotics and artificial intelligence seems to indicate a future shift in our human-centered social paradigm towards a greater inclusion of artificial cognitive agents in our everyday environments. Understanding AI agents goes beyond the interpretation of a specific algorithm and requires analyzing the interactions between agents and with their surroundings. Combining AI and behavioral science is then seen in its true potential, as an ethical way to improve people's lives, where end users can feel safe when sharing personal data for transparent and relevant services.